Tools are needed for managing the credit risk and return of a large scale financial portfolio. This means measuring the risk and return characteristics of the portfolio as it currently exists, exploring specific opportunities to add or subtract from the portfolio, and looking at the effect of large scale changes in the portfolio to assess new tactical and strategic directions.
Portfolio models relying upon Markowitz equations are known in the art. These prior art equations include a return equation where the return associated with the portfolio, .mu..sub.p, is determined by the sum of the initial values w.sub.i (expressed either in absolute or relative terms) times the mean return for the assets .mu..sub.i, which can be expressed as : EQU .mu..sub.p =.SIGMA.w.sub.i *.mu..sub.i (Equation 1).
The Markowitz equations also include a risk equation where the risk associated with the portfolio (r.sup.2.sub.p) is determined as a function of the initial values (w.sub.i, w.sub.j), expressed either in absolute or relative terms, the standard deviation of asset returns (r.sub.i, r.sub.j), and the correlation of the random returns of assets i and j (p.sub.ij), which can be expressed as: EQU r.sup.2.sub.p =.SIGMA..SIGMA.w.sub.i w.sub.j r.sub.i r.sub.j p.sub.ij(Equation 2).
Although Markowitz equations and other portfolio analysis equations (collectively referred to as portfolio analysis expressions) are known in the art, there is still great difficulty in establishing accurate input values for portfolio analysis expressions. Thus, it would be highly desirable to provide improved modeling of the risk and return associated with a financial portfolio by establishing a technique for accurately assigning input values that are processed in accordance with portfolio analysis techniques.